Recognition of activities, typically the activities of daily living (ADLs) in smart home or office environments tend to utilize two distinct paradigms. The wearable sensing approach typically utilizes sensors embedded in wearable computing devices, such as smart watches and smart glasses, to capture fine-grained, individual specific locomotion and gestural activities. However, energy remains a critical challenge for continuous sensing: with low-capacity batteries, wearable devices requiring frequent charging. In contrast, installing sensors in everyday ‘smart objects’, such as kitchen cabinets, household appliances and office equipment, supports ADL detection via indirect observations on human interaction with such objects, but cannot provide individual-specific insights in multi-tenanted environments.